This page covers everything you need to install Lumina AI and its dependencies from scratch. It explains each required package, documents the model and asset files the app expects to find at runtime, and provides platform-specific instructions for optional features like voice input and audio playback.Documentation Index
Fetch the complete documentation index at: https://mintlify.com/diazdavilajesus16-stack/IA-LUMINA/llms.txt
Use this file to discover all available pages before exploring further.
Python version requirement
Lumina AI requires Python 3.8 or higher. You can check your current version with:Core installation
Install all Python dependencies declared inrequirements.txt with a single command:
| Package | Version | Purpose |
|---|---|---|
streamlit | >=1.28.0 | Web UI framework that serves the chat interface |
PyPDF2 | >=3.0.0 | Extracts text from uploaded PDFs for context-aware answers |
gTTS | >=2.3.0 | Converts Lumina AI’s responses to spoken audio (Google Text-to-Speech) |
SpeechRecognition | >=3.10.0 | Transcribes microphone input to text |
pydub | >=0.25.0 | Converts audio formats (e.g., WebM to WAV) before transcription |
ML engine dependencies
chatbot.py — the neural network inference engine — requires three additional packages that are not listed in requirements.txt. Install them separately if you need to run chatbot.py directly or retrain the model:
| Package | Purpose |
|---|---|
tensorflow / keras | Loads and runs the trained chatbot_model.h5 neural network |
numpy | Builds the bag-of-words input vectors for the model |
nltk | Tokenizes and lemmatizes user input before inference |
Platform-specific notes
Voice input (SpeechRecognition + pydub) requires ffmpeg to convert audio formats. The core chat and PDF features work without it.
- Windows
- macOS
- Linux
Install ffmpeg using Chocolatey:Alternatively, download the ffmpeg binary from ffmpeg.org and add it to your
PATH manually.Required files
Lumina AI expects the following files to be present in the project root at runtime.Model files
| File | Description |
|---|---|
chatbot_model.h5 | The trained Keras Sequential model (three Dense layers with Dropout, softmax output) |
words.pkl | Pickle of the sorted vocabulary list built from intent patterns during training |
classes.pkl | Pickle of the sorted intent class labels built from intent tags during training |
respuestas.json | JSON file defining all intents: each intent has a tag, a list of patterns, and a list of responses |
UI assets
The following image files are loaded byMain.py at startup. The app encodes them as Base64 and embeds them directly in the HTML so they render without a web server.
assets/robot_girl.png
Avatar image displayed in the sidebar and alongside assistant messages in the chat window.
assets/galaxy.png
Background image for the galactic UI theme. If missing, the app falls back to a CSS radial-gradient background automatically.